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Comment by laurels-marts | Hacker News Reader

Damn this is exciting. I love that gpt models are much faster, efficient and cheaper than Claude models. They are so fast even on high/xhigh that I don’t find myself using the parallel agent setup anymore much since its cognitively less demanding to just follow along what the model is doing and most tasks it will complete in <5-<10mins anyway.

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This is because GPT-5.6 is just a more posttrained version of GPT-5.5, not a bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.

GPT-5.6 --------> GPT-5.6 Sol

GPT-5.6-mini ---> GPT-5.6 Terra

GPT-5.6-nano ---> GPT-5.6 Luna

Two important things to note, if you want to verify what I say/correct me:

GPT-5.6 Terra actually scores worse than GPT-5.5 on many benchmarks. It's not GPT-5.5 trained with more compute; it's basically GPT-5.6-mini that's been distilled from GPT-5.6 full size. Remember, GPT-5.4-mini had almost the same benchmarks as GPT-5.2 after all.

Opus 4.8 runs at ~90 tokens per second. Fable 5 runs at ~40 tokens per second on from Anthropic, because it's a bigger/slower model. A few days after the release, when the dust dies down, look at how many tokens/second GPT-5.6 Sol is running at. I will bet it's the about same as GPT-5.5, and not half the speed. (OpenAI is not incentivized to slow down the model for paying customers). But the model tokens/sec will be a big clue- if OpenAI is charging more money for the same sized model or not.

bigger doesn't mean better, chill out

That's true but size of LLMs has been strongly correlated with their "intelligence".

not just that, but the entire industry spend several years seeking investment on the "pure" idea that they just need more compute and more parameters to reach AGI.

And the "business" obvious is still doing that but the science and implementation has be realizing that this just isn't true. They're not getting AGI out of a single LLM by itself.

They clearly know it but have to pretend otherwise to keep the money flowing.

right, but the business arm will always be dominant. I see what Chinese models are doing as the same as japanese car models in the 80s: producing smaller, more efficient products that address the realities of the "total addressable market" that no business model would support. They're, unfortunately, providing public value where the US and Europe used to tread.

There's a lot to make efficient, but it should be clear to everyone that just throwing compute at larger models isn't going to magically make it rain.

In this case, it does actually.

> This is because GPT-5.6 is just a more posttrained version of GPT-5.5, not a bigger model than GPT-5.5.

What is this very confident assumption based on?

If it were fully trained from scratch, you'd expect to see a major version bump. The other point releases have been fine tunes or post trains.

but 5.5 was also no major version bump

Also tokens/sec are not only dependent on parameter size, so that is kind of a brittle theory.

do we know that Mythos is in fact a bigger model then opus?

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